Abstract My last column ended with some comments about Kuhn and word2vec. Word2vec has racked up plenty of citations because it satisifies both of Kuhn’s conditions for emerging trends: (1) a few initial (promising, if not convincing) successes that motivate early adopters (students) to do more, as well as (2) leaving plenty of room for…
Natural Language Engineering Template
Write in a clean editor, then format for Natural Language Engineering in one click — DocuGuru applies the official Cambridge University Press template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Natural Language Engineering format
Natural Language Engineering is a peer-reviewed journal published by Cambridge University Press, covering Natural Language Processing Techniques, Topic Modeling, Speech and dialogue systems.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, Natural Language Engineering 12 (2023) 45–58.
Formats any DOI in the closest standard style — Natural Language Engineering has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Natural Language Processing Techniques Topic Modeling Speech and dialogue systems Diverse Scientific and Economic Studies Educational Robotics and Engineering |
| ISSN | 1351-3249 |
| Citation impact (2-yr) | 7.5 |
| h-index | 85 |
| i10-index | 421 |
| Total citations | 31,146 |
| Top institutions publishing here | University of Sheffield |
| Journal website | www.cambridge.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Natural Language Engineering per year
Citation impact of Natural Language Engineering by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Natural Language Engineering
IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly…
Abstract This paper identifies some linguistic properties of technical terminology, and uses them to formulate an algorithm for identifying technical terms in running text. The grammatical properties discussed are preferred phrase structures: technical terms consist mostly of noun phrases containing adjectives, nouns, and occasionally prepositions; rerely do terms contain verbs, adverbs, or conjunctions. The discourse…
Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that…
Abstract This paper is an introduction to natural language interfaces to databases (NLIDBS). A brief overview of the history of NLIDBS is first given. Some advantages and disadvantages of NLIDBS are then discussed, comparing NLIDBS to formal query languages, form-based interfaces, and graphical interfaces. An introduction to some of the linguistic problems NLIDBS have to…